code: def create_video_reader_pipeline(sequence_length, files, crop_size):
      images = fn.readers.video(
          device="gpu",
          filenames=files,
          sequence_length=sequence_length,
          normalized=False,
          random_shuffle=True,
          image_type=types.RGB,
          dtype=types.UINT8,
          initial_fill=16,
          pad_last_batch=True,
          name="Reader",
      )
      images = fn.crop(
          images,
          crop=crop_size,
          dtype=types.FLOAT,
          crop_pos_x=fn.random.uniform(range=(0.0, 1.0)),
          crop_pos_y=fn.random.uniform(range=(0.0, 1.0)),
      )
  
      images = fn.transpose(images, perm=[3, 0, 1, 2])
  
      return images
  
file_path: DALI/docs/examples/use_cases/video_superres/dataloading/dataloaders.py
imports: import sys
  import copy
  from glob import glob
  import math
  import os
  import torch
  from torch.utils.data import DataLoader
  from dataloading.datasets import imageDataset
  from nvidia.dali.pipeline import pipeline_def
  from nvidia.dali.plugin import pytorch
  import nvidia.dali.fn
  import nvidia.dali.types
label_desc: Use image node_id 1 for calling a global function with example usage: create_video_reader_pipeline(sequence_length, files, crop_size) and returns: images